E-commerce app users exhibit behaviors that are inherently logically consistent. A series of multi-scenario user behaviors interconnect to form the scene-level all-domain user moveline, which ultimately reveals the user's true intention. Traditional CTR prediction methods typically focus on the item-level interaction between the target item and the historically interacted items. However, the scene-level interaction between the target item and the user moveline remains underexplored. There are two challenges when modeling the interaction with preceding all-domain user moveline: (i) Heterogeneity between items and scenes: Unlike traditional user behavior sequences that utilize items as carriers, the user moveline utilizes scenes as carriers. The heterogeneity between items and scenes complicates the process of aligning interactions within a unified representation space. (ii) Temporal misalignment of linked scene-level and item-level behaviors: In the preceding user moveline with a fixed sampling length, certain critical scene-level behaviors are closely linked to subsequent item-level behaviors. However, it is impossible to establish a complete temporal alignment that clearly identifies which specific scene-level behaviors correspond to which item-level behaviors. To address these challenges and pioneer modeling user intent from the perspective of the all-domain moveline, we propose All-domain Moveline Evolution Network (AMEN). AMEN not only transfers interactions between items and scenes to homogeneous representation spaces, but also introduces a Temporal Sequential Pairwise (TSP) mechanism to understand the nuanced associations between scene-level and item-level behaviors, ensuring that the all-domain user moveline differentially influences CTR predictions for user's favored and unfavored items. Online A/B testing demonstrates that our method achieves a +11.6% increase in CTCVR.

翻译：电商应用用户的行为表现出内在的逻辑一致性。一系列多场景用户行为相互连接，形成场景级全领域用户动线，最终揭示用户的真实意图。传统点击率预测方法通常聚焦于目标物品与历史交互物品之间的物品级交互，但目标物品与用户动线之间的场景级交互仍未被充分探索。在建模与先验全领域用户动线的交互时存在两大挑战：（i）物品与场景间的异质性：不同于以物品为载体的传统用户行为序列，用户动线以场景为载体。物品与场景间的异质性使得在统一表示空间中对齐交互的过程复杂化。（ii）关联场景级与物品级行为的时间错位：在固定采样长度的先验用户动线中，某些关键场景级行为与后续物品级行为紧密关联，但无法建立完整的时间对齐关系以明确识别哪些具体场景级行为对应哪些物品级行为。为应对这些挑战并开创性地从全领域动线视角建模用户意图，我们提出全领域动线演化网络（AMEN）。AMEN不仅将物品与场景间的交互迁移至同质表示空间，还引入时间序列成对（TSP）机制以理解场景级与物品级行为间的细微关联，确保全领域用户动线对用户偏好与非偏好物品的点击率预测产生差异化影响。在线A/B测试表明，我们的方法实现了CTCVR提升11.6%。